Use case — Pharma R&D
From unknown peaks to a ranked shortlist
NMR compound identification still stalls discovery, impurity work, and metabolite studies because a specialist has to interpret each spectrum by hand. Spectra turns that first pass into a ranked list of candidates, with confidence scores, so experts spend time on the decision rather than on hunting peaks.
Where identification slows down
An unknown or only partly known compound can take days or weeks to assign. The work is hard to repeat across analysts, and the queue grows every time a new library, impurity, or extract arrives.
Experts stuck on first-pass review
Senior chemists spend hours comparing candidates instead of confirming the structure that actually matters.
Decisions wait on the spectrum
Discovery, impurity investigations, and validation stall while the identification sits in an expert queue.
The method does not scale
A manual workflow cannot stay consistent across larger compound sets, sample batches, or multiple project teams.
What Spectra does with your spectrum
Upload an NMR spectrum to spectra.rombo.ai. The model highlights patterns, proposes structures, and ranks them. Your chemists keep the final call, with a shortlist instead of a blank page.
Upload the spectrum
Bring NMR data and sample context into one review workspace instead of scattered files and notebooks.
Match spectral patterns
The model flags the features that discriminate candidates, so you skip the repetitive first pass.
Rank candidates
Get a scored shortlist of plausible structures so expert time goes to the options that fit the data.
Review and confirm
Chemists stay accountable for assignment, validation, and the project decision.
What changes in the lab
- Cut identification from days or weeks toward about one hour in a focused proof of concept.
- Spend less expert time on peak picking and candidate comparison.
- Get the same ranking logic across analysts, projects, and compound classes.
- Handle larger libraries without dropping scientific review.
- Keep a clear trail from spectrum to shortlist to the reviewed outcome.
Who it helps
- Discovery teams get structural hypotheses sooner and prioritize the next experiment faster.
- Analytical chemists review and validate instead of screening every peak by hand.
- Impurity teams move from an unknown signal to a candidate explanation without waiting in the expert queue.
- R&D leads get a repeatable workflow for high-value assignments.